Support Vector Machines

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# <span style="font-size:48pt;">Support Vector Machines</span>
## 🚧 🛣️ 🦿
### Applied Machine Learning in R <br />Pittsburgh Summer Methodology Series
### Lecture 4-A     July 22, 2021

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# Overview

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## Maximal margin classifier

How do we separate these classes?

<img src="data:image/png;base64,#maxmargin1.png" width="80%" />

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## Maximal margin classifier

With one feature, we need to find a point that separates the classes

<img src="data:image/png;base64,#maxmargin2.png" width="80%" />

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## Maximal margin classifier
But there are many possible decision points, so which should we use?

<img src="data:image/png;base64,#maxmargin3.png" width="80%" />

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## Maximal margin classifier
One option is to find the point with the **maximal margin** between the classes

<img src="data:image/png;base64,#maxmargin4.png" width="80%" />

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## Maximal margin classifier

With two features (i.e., 2D), we need a decision **line** rather than a point

<img src="data:image/png;base64,#maxmargin5.png" width="80%" />

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## Maximal margin classifier

With three features (i.e., 3D), we need a decision **plane**

<img src="data:image/png;base64,#3d_plane.gif" width="50%" />

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## Maximal margin classifier

With one feature, we need a 1D plot and a decision **point**

With two features, we need a 2D plot and a decision **line**

With three features, we need a 3D plot and a decision **plane**

With four or more features, we can't plot it but we need a decision **hyperplane**

.footnote[[1] Technically, all of these are hyperplanes of different sizes, but we usually reserve the term for 4+ dimensions.]

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## Support vector classifier

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## Support vector machine

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## Support vector regression

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# Kernels

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## Overview of kernerls

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## Polynomial kernel

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## Radial basis function (RBF) kernel

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## Hyperparameters for SVM

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## Applied Example

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## Live Coding Activity

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## Hands-on Activity

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## Break and timer